Candidate: Rhonda Szczesniak, Ph.D., is a trained mathematical statistician. Her overarching career goal is to develop an independent clinical-research career translating statistical innovation for personalized medicine into clinical trials and practice, thereby improving the care and outcomes of patients with cystic fibrosis (CF) and other lung diseases. She is an Associate Professor in the University of Cincinnati (UC) Department of Pediatrics and jointly appointed to the Division of Biostatistics & Epidemiology and the Division of Pulmonary Medicine at Cincinnati Children's Hospital Medical Center (CCHMC). The Candidate's commitment to biomedical research began with developing a functional data analysis method to classify synaptic transmission wave-forms. Her faculty-level pursuit of biomedical research has led to successful projects in data-intensive statistical methods applied to research in lung disease and cardiovascular health. Career Development: To become an independent clinical and translational researcher in quantitative biomedicine, the proposed K25 career development plan will build upon the Candidate's early career development and experience with focus on five key areas: 1) CF lung disease markers and clinical management; 2) pulmonary outcomes research; 3) computational medicine; 4) clinical trials; 5) R01-level grantsmanship. She will expand her understanding in the 1st area through hands-on clinical training in pulmonary function testing, on campus didactic training in disease-specific clinical/translational research, select attendance at presentations on clinical, basic and translational CF projects from local and invited researcher leaders, and participation in a CF biomarker consortium. She will establish expertise in the 2nd area through on-campus didactic training in mixed methods research and participation in a collaborative, patient-centered pulmonary outcomes research lab. She will develop and apply advanced skills in the 3rd area by apprenticing in a computational medicine lab, training in healthcare interface design, and completing on-campus coursework in intelligent data analysis and decision analysis. Her expertise in the 4th area will be gained through local and NIH training. She will acquire academic and professional skills for the 5th area by taking an on-campus course in leadership in clinical/translational research and participating in early-stage CF investigator forums, and faculty development seminars. She will receive training in the responsible conduct of research through formal coursework and CF clinic/lab interactions. Mentors/Environment: The Candidate's sponsor is a well-established CF clinical and translational researcher and trials investigator. Together, they have assembled a strong team to guide her through the proposed training and research activities. Her co-sponsors are established investigators in pulmonary outcomes research and computational medicine. Her on-campus consultants have extensive clinical research and training experience in measuring markers of lung disease and CF care delivery. Her external consultant is an R01- level researcher with accomplishments in the Candidate's statistical field, functional data analysis. Her research and training will utilize rich intellectual and physical resources available through the distinguished UC/CCHMC CF research environment, including the Pulmonary Function Testing Lab; nationally ranked Boomer Esiason Cystic Fibrosis Center; Computational Medicine Center; Office for Clinical/Translational Research; CF Research and Development Program; top-performing CF Clinical/Translational Research Facility. Research: Progressive lung disease is the primary cause of death in individuals with cystic fibrosis (CF). Rapid decline, characterized by accelerated loss of lung function, is a ubiquitous event in the lives of CF patients. Identifying those at highest risk for rapid decline is a signifiant gap in CF care, and offers the opportunity to intervene prior to irreversible lung damage. This gap is exacerbated by the paucity of individualized predictive data on rapid decline and continued use of linear statistical approaches to model nonlinear CF disease progression. Functional data analysis is a statistical method that has been used to characterize nonlinear phenomena and elucidate complex pathophysiological relationships in different disease states. The overall objective of this research, which serves as the next step to achieve the Candidate's overarching career goal, is to utilize a well-maintained, rich CF registry and prospective study data to accurately forecast the onset of rapid decline in individual patients, and to develop a feasible medical-monitoring tool that positively impacts CF point-of-care decision-making. The central hypothesis is that translating intensive patient-level data through functional data analysis into a medical monitoring system and accurately forecasting lung disease progression to help prioritize pulmonary interventions will improve individualized care. Specific aims are to 1 characterize the phenotype of CF patients at increased risk of early, rapid decline; 2) design a decision support tool to moni- tor real-time lung-function decline for personalized clinical management of CF patients; 3) validate a model-based detection algorithm for rapid decline that informs CF point-of-care decisions. With systems to predict rapid decline, better prospective treatment decisions will become possible, resulting in better patient outcomes. Summary: Capitalizing on the unique combination of resources available to the Candidate and her quantitative training, this project will rapidly position her to submit an R01 to conduct a definitive efficacy trial of clinical forecasting systems for personalized CF therapies and establish a pipeline of R01-level research to design and evaluate medical monitoring interventions in other lung diseases and disorders.